Data Engineering on Microsoft Azure 

Data Engineers: Learn to build and maintain data structures and architectures

Data Scientists: Understand how to manipulate large data sets and use them to validate and optimize business strategies

IT Professionals: Gain insights into the latest Azure data platform technologies

Business Analysts: Learn to make data-driven decisions using comprehensive data models

Database Administrators: Understand how to manage and optimize databases on Azure

Explore compute and storage options for data engineering workloads in Azure

Design and Implement the serving layer

Understand data engineering considerations

Run interactive queries using serverless SQL pools

Explore, transform, and load data into the Data Warehouse using Apache Spark

Perform data Exploration and Transformation in Azure Databricks

Ingest and load Data into the Data Warehouse

Transform Data with Azure Data Factory or Azure Synapse Pipelines

Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines

Optimize Query Performance with Dedicated SQL Pools in Azure Synapse

Analyze and Optimize Data Warehouse Storage

Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

Perform end-to-end security with Azure Synapse Analytics

Perform real-time Stream Processing with Stream Analytics

Create a Stream Processing Solution with Event Hubs and Azure Databricks

Build reports using Power BI integration with Azure Synapse Analytics

Perform Integrated Machine Learning Processes in Azure Synapse Analytics

This training offers a deep dive into data engineering patterns and practices on Azure

It covers core compute and storage technologies, design of analytical serving layers, and data engineering considerations

Students will learn to interactively explore data in a data lake, ingest data using Apache Spark in Azure Synapse Analytics or Azure Databricks, and transform data using the same technologies

The training also covers how to monitor and optimize the performance of analytical systems, implement security measures, and use data in an analytical system to create dashboards or build predictive models in Azure Synapse Analytics